Whole-genome and Epigenomic Landscapes of Malignant Gastrointestinal Stromal Tumors Harboring KIT Exon 11 557–558 Deletion Mutations

Gastrointestinal stromal tumors (GIST) with KIT exon 11 deletions involving in codons 557–558 (KIT Δ557–558) exhibit higher proliferation rates and shorter disease-free survival times compared with GISTs with other KIT exon 11 mutations. We analyzed 30 GIST cases and observed genomic instability and global DNA hypomethylation only in high-risk malignant GISTs with KIT Δ557–558. Whole-genome sequencing revealed that the high-risk malignant GISTs with KIT Δ557–558 (12 cases) had more structural variations (SV), single-nucleotide variants, and insertions and deletions compared with the low-risk, less malignant GISTs with KIT Δ557–558 (six cases) and the high-risk (six cases) or low-risk (6 cases) GISTs with other KIT exon 11 mutations. The malignant GISTs with KIT Δ557–558 showed higher frequency and significance in copy number (CN) reduction on chromosome arms 9p and 22q, and 50% of them had LOH or CN-dependent expression reduction in CDKN2A. In addition, SVs with driver potential were detected in 75% of them, in which AKT3 and MGMT were recurrently identified. Genome-wide DNA methylation and gene expression analyses showed global intergenic DNA hypomethylation, SNAI2 upregulation, and higher expression signatures, including p53 inactivation and chromosomal instability, as characteristics of malignant GISTs with KIT Δ557–558 that distinguished them from other GISTs. These genomic and epigenomic profiling results revealed that KIT Δ557–558 mutations are associated with increased genomic instability in malignant GISTs. Significance: We present genomic and epigenomic insights into the malignant progression of GISTs with KIT exon 11 deletions involving in 557–558, demonstrating their unique chromosomal instability and global intergenic DNA hypomethylation.


Introduction
Gastrointestinal stromal tumors (GIST) are the most common mesenchymal tumors of the gastrointestinal tract, with a worldwide annual incidence of six and/or metastasize to the liver (3). The prognosis for localized GISTs has been assessed by risk stratification schemes to identify tumors that are more likely to recur at distant sites after curative surgery (1). The modified NIH consensus criteria classify GISTs according to size, mitotic count, tumor location, and tumor rupture (4) and are useful for identifying patients who may benefit from adjuvant therapy (5).
Gain-of-function mutations in KIT occur in approximately 70% of GISTs (1).
GISTs carrying KIT exon 11 mutations with deletions at codon 557 and/or 558 have a poorer prognosis than those with mutations at other sites (6)(7)(8)(9). Deletions at both codons 557 and 558 promote liver metastasis (10) and are thought to be required for malignant transformation (11). The malignant progression of GISTs has also been associated with chromosomal changes, including deletions in chromosome arms 1p, 13q, 14q, 15q, and 22q; gains in chromosomes 4 and 5 (12)(13)(14); and genetic alterations, including mutations, copy-number (CN) abnormalities, and aberrant expression of cell cycle-related genes (p, CDKNA, and RB; ref. 15) and PI3K pathway-related genes (PIKCA and PTEN; ref. 16). Epigenomic analysis has identified promoter hypermethylation of cell cycle-related genes (17) and LINE- DNA hypomethylation in high-risk and malignant GISTs (18). However, these results are based on comparative analyses between malignant and less malignant cases, and there are no reports of systematic genomic and epigenomic analysis specifically targeting KIT exon 11 deletions involving in codons 557-558 (KIT 557-558).
Whole-genome sequencing (WGS) is a powerful tool capable of revealing the diversity and complexity of global genomic alterations, such as structural variations (SV) and copy-number alterations (CNA) as well as mutations. Thus, we performed WGS along with genome-wide DNA methylation and gene expression analyses in 30 GIST cases to determine the genomic and epigenomic differences between KIT 557-558 and other KIT exon 11 mutations. This included signature analysis of the expression data, and we discuss the relationship between the hypoxia signature and the recently approved Hsp90 inhibitors (19).

Clinical Samples
Tumor and matched normal tissue samples along with whole-blood samples were obtained from patients receiving surgical treatment at the Shizuoka Cancer Center who were participants in the Project HOPE multi-omics study (20). The study was approved by the Institutional Review Board of the Shizuoka Cancer Center (authorization number: [25][26][27][28][29][30][31][32][33]. All patients agreed to participate in the study and provided written informed consent. All experiments using clinical samples were performed in accordance with the ethical principles of the Declaration of Helsinki. Table 1 presents clinicopathologic data of the 30 patients with GIST in our study cohort. KIT mutations were assessed by whole-exome sequencing (WES), panel sequencing, Sanger sequencing, and the Integrative Genomics Viewer (IGV) tool (16). The prognosis for GISTs was assessed on the basis of a risk stratification scheme using the modified NIH consensus classification, which considers tumor size, mitotic count, tumor location, and tumor rupture (4).

WGS
Following DNA extraction from tumor and matched peripheral blood samples, we constructed DNA libraries using a TruSeq DNA PCR-Free High Throughput Library Prep Kit (20015963; Illumina) with 1 μg DNA according to the manufacturer's instructions. Then we performed whole-genome 150-bp paired-end WGS using a NovaSeq 6000 System (Illumina). We converted the resultant raw data to FASTQ format with Bcl2fastq v2.20 (Illumina) and then used DRAGEN Bio-IT Platform v3.9 (Illumina) for mapping sequenced reads to reference human genome hs37d5, marking duplicated reads, variant calling, and calculating quality metrics.

Variant Calling and Annotation
We classified variants as small variants, including single-nucleotide variants (SNV) and insertions or deletions Indels; (≤50 bases), or SVs (>50 bases). For SNVs and Indels, we identified somatic variants using DRAGEN Small Variant Caller. Variants with a low sequence depth in normal and/or tumor samples, as well as those with a low variant depth and/or variant allele frequency in tumor samples, were excluded from the downstream analysis. We used Ensembl Variant Effect Predictor v104 for variant annotation (21) and analyzed the drivability and actionability of variants with the in-house pipeline Shizuoka Multi-omics Analysis Protocol (SMAP; ref. 22). This pipeline evaluated and classified the annotated variants into five tiers according to the reliability of supporting information by sequentially comparing alterations among multiple databases, as described previously (22).
We detected SVs using DRAGEN Structural Variant Caller and applied the python script convertInveresion.py, which is provided as part of manta v1.6 (23), to extract inversions. Those SVs that were flagged as IMPRECISE were discarded. In addition, those for which at least one breakpoint was located within 100 bp of (i) segmental duplications, (ii) microsatellites, (iii) simple repeats, (iv) low complexity regions, and (v) ENCODE blacklist genomic regions were discarded. For each breakpoint, the gene symbol, exon/intron number, transcript accession number (RefSeq and Ensembl), and relationship to the coding exon frame were annotated by an in-house developed pipeline. SVs were categorized into five classes (translocation, insertion, deletion, duplication, and inversion) based on the mapping information for a read pair. The drivablity and actionability of SVs were assessed using SMAP. In addition to tier 1 (driver SVs) and tier 2 (likely driver SVs) annotation, SVs were annotated as tier 3 (predicted driver SVs) if they disrupted the coding sequence of a tumor suppressor gene (TSG).

Mutational Signatures
We used the non-negative matrix factorization method to detect mutational signatures in WGS samples, and they were subsequently analyzed using Mu-tationalPatterns v3.0.1 software (24). Briefly, a single-base substitution (SBS) profile of 96 combinations of base substitutions and neighboring bases was constructed for each sample. In the same way, an indel profile of 83 combinations of variant type, length, and sequence features was constructed for each sample.
To obtain insight into the mutational signatures, these profiles were decomposed into an optimal combination of the Catalogue of Somatic Mutations in AACRJournals.org Cancer Res Commun; 3(4) April 2023

CNAs
CNAs were identified using the DRAGEN Copy Number Variant pipeline. Those variants that were flagged as PASS were extracted and used in the subsequent analyses. In cases where DRAGEN could not reliably estimate tumor content from the WGS dataset, those variants flagged as lowModelConfidence in the FILTER field in the variant call format (VCF) file were also extracted.
Gene-specific CNs were determined by annotation against known genes in Ensembl v104. Tumors with whole-genome duplication (WGD) were defined as those where ModelSource was described as DEPTH+BAF_DOUBLED in the VCF file. The homologous recombination deficiency (HRD) score was calculated using DRAGEN HRD caller and defined as the sum of the following: (i) LOH score, (ii) telomeric allelic imbalance score, and (iii) large-scale state transition score. The CN profile of a sample was calculated as the relative length of 48 combinations of copy-number variations (CNV), and the profile was constructed from six CN categories (0, 1, 2, 3-4, 5-8, and ≥9), five CNV length categories (0-100 kb, 100 kb-1 Mb, 1-10 Mb, 10-40 Mb, and >40 Mb), and three CNV types (homozygous deletion, CNV with LOH, CNV without LOH). The profile was decomposed into COSMIC CNV signatures in the same manner as described above for mutational signatures. The ploidy score was calculated as the sum of the number of autosomal chromosome arms gained or lost.

DNA Methylation Analysis
We generated bisulfite-converted DNA for methylation analysis using an EZ DNA Methylation Kit (D5001; Zymo Research) with 500 ng aliquots of DNA. Then, we performed a comprehensive DNA methylation analysis using the Infinium MethylationEPIC BeadChip Kit, covering 866,895 methylation sites (WG-317-1001; Illumina). Next, the specifically hybridized DNA was fluorescently labeled by a single-base extension reaction and detected using an iScan System array scanner (Illumina) in accordance with the manufacturer's protocols. Finally, the data were assembled using GenomeStudio Methylation Module v1.8 (Illumina) software. The methylation levels of the CpG sites were represented by β values ranging from 0 (completely unmethylated) to 1 (completely methylated), and the data were analyzed using the Subio Platform (https://www.subioplatform.com), Excel 2021 (Microsoft), and GraphPad Prism v8.3.0 (GraphPad Software) software. CpG sites on the sex chromosomes were excluded from the analysis, leaving 846,927 probes. To analyze the intergenic methylation levels, probes for the CpG islands around the transcription start sites (TSS) were excluded, leaving 741,629 probes. For the clustering analysis, probes whose difference was within 0.1 of the average β values in all 23 samples were excluded, leaving 648,741 probes, of which 12,000 were randomly selected using the RAND function in Excel. For global intergenic methylation analysis, 741,629 probes were binned in 300-kb lengths to determine the average β values. Methylation levels at promoter regions were assessed using the average β value of probes located on the CpG islands around TSS.

Gene Expression Analysis
Total RNA was isolated from minced tissue samples using a miRNeasy Mini Kit (217004; Qiagen) as described previously (25)

Gene Expression Signatures
The expression signatures/scores were calculated from z-scores from the mean log 2 expression values of the genes corresponding to each signature, including TP53 inactivation (28), chromosomal instability (CIN; ref. 29), and hypoxia (30). The genes and probes for each signature are listed in Supplementary   Table S1.

Statistical Analysis
We used Welch t test and Fisher exact test for comparisons between two groups and Pearson correlation coefficient to compare two variables. P values <0.05 were considered statistically significant.

Data Availability
Sequencing, expression, and methylation data have been deposited in the National Bioscience Database Center (NBDC) under accession number JGAS000604. Access can be requested through the NBDC application system (https://humandbs.biosciencedbc.jp/en/data-use). Other data generated in this study are available within the article and its Supplementary Data.

WGS
We performed WGS of 60 genomes from DNA isolated from tumor tissue and matched blood samples from 30 patients with GIST with pathogenic KIT mutations in exon 11 ( Supplementary Fig. S1) to identify the genomic characteristics contributing to the development of KIT 557-558 in GISTs. The presence of KIT mutations was identified by WES, panel sequencing, Sanger sequencing, and the IGV tool, and some cases had been reported previously (16). The 30 GIST cases were classified into the following four groups according to the presence or absence of KIT 557-558 and the grade based on risk criteria using the modified NIH consensus classification (4) Table S2).

Somatic Mutational Landscape
The somatic mutational landscape differed between malignant GISTs with KIT 557-558 and other cases ( Fig. 1; Supplementary Figs. S1 and S4; Supplementary Table S2; Supplementary Data S1 and S2). GISTs with KIT 557-558 in group A exhibited significantly more SVs, SNVs, and Indels than other cases in group B, C, and D, respectively (P < 0.05), with the exception of SVs in group B. The mutational signatures, including SBS and small insertions and deletions (ID; also known as indels), were compared with signatures from the COSMIC database (31). SBS1, SBS5, and SBS40, known as "clock-like" signatures, were predominant among all samples. SBS89, whose etiology is unknown, was detected in 57% of samples (>5% contribution). Signatures more common in malignant GISTs with KIT 557-558 (group A) included SBS8, SBS26, and ID1 and ID2 contributed more than 10% in all samples except one sample with ID2. Only ID1 showed a significantly higher contribution in group A compared with the other groups (P < 0.0001). ID1 is characteristic of predominant insertions of thymine at thymine mononucleotide repeats. ID2 is predominantly composed of deletions of thymine at thymine mononucleotide repeats. Alexandrov and colleagues proposed that ID1 and ID2 were probably due to slippage of either the nascent (ID1) or template (ID2) strand during DNA replication at poly-T tracts (30). In addition, these signatures were found in most samples from most cancer types, but were particularly common in colorectal, stomach, endometrial, and esophageal cancers and in diffuse large B-cell lymphoma. Collectively, these molecular landscapes on the various KIT exon 11 mutations in GISTs indicate genomic instability in malignant GISTs with KIT 557-558.

Somatic CNAs
Next, we investigated whole-genome level CNAs in GISTs ( Fig. 2; Supplementary Table S2; Supplementary Data S3). Among the 30 cases, two (nos. 1 and 25) showed CN increase across the entire region (ploidy >3), with WGD detected in case 1 and the highest HRD score in case 25. Furthermore, CN signature analysis also revealed that cases 1 and 25 showed CN17 and CN2, indicative of HRD and a tetraploid genome, respectively. CN2 was also shown in two other cases (nos. 9 and 15).
Deletions in chromosome arms 14q, 22q, 1p, 9p, and 15q have been frequently observed in GISTs, and these cytogenetic events are necessary for the malignant transformation of micro-GISTs into tumors (12). Our WGS analysis also identified these deletions (Fig. 2) In addition, comparing the frequency of deletions between high-risk (group A and B) and low-risk (group C and D) GISTs revealed a significant difference

Whole-genome Landscape of Driver Alterations
KIT mutations, along with PDGFRA mutations, are involved in the earliest events in GIST development. As described previously in this and other studies, CNAs and other mutations have been associated with GIST progression.
In particular, activation of the cell-cycle pathway by RB and TP mutations and the PI3K pathway by PIKCA and PTEN mutations were suggested as involved in the malignant transformation of GISTs (15,16). Overall, our WGS analysis revealed 15 driver gene mutations from 13 genes in 12 cases (Fig. 3).
Only RB mutations were recurrently detected (three cases). In the 18 malignant cases (group A and B), 11 mutations from nine genes were found in eight cases. Furthermore, eight mutations from seven genes were identified in 50% Currently, there are no reports on SV detection in GISTs by comprehensive WGS analysis. In this study, we detected 1,257 SVs in all 30 cases and evaluated them as driver SVs using our in-house pipeline SMAP (Supplementary Data S1). No tier 1 pathogenic driver SVs were identified, but 42 SVs with driver potential were identified, including four tier 2 SVs and 38 tier 3 SVs. Of the 42 SVs, after excluding SVs that contained the same genes, 32 potential driver SVs from 29 genes were detected in 14 of the 30 GIST cases. The genes recurrently detected in potential driver SVs were AKT, LRPB, and MGMT. Because the number of SVs was significantly higher in the malignant cases with KIT 557-558 ( Supplementary Fig. S4), potential driver SVs were detected in 75% (nine of 12 cases) of these cases (group A), in which SVs were recurrently involved in AKT and MGMT.
As mentioned above, the deletion in chromosome 9p was characteristic of malignant GISTs with KIT 557-558 and CDKNA deletion was the most notable aberration in this region. We identified a decrease in CDKNA CN in six of the 12 malignant cases with KIT 557-558. Among them, loss of function was predicted in five cases due to decreased CDKNA expression and in the remaining case (no. 10) due to LOH. The other five cases showed reduced CDKNA mRNA expression. Decreased CDKNA expression was also observed in another sample, which had no mutations or SVs in CDKNA, possibly due to promoter hypermethylation (32,33). Collectively, the loss-offunction alterations of CDKNA occurred in seven of the 12 malignant cases with KIT 557-558. Two TSGs thought to be involved in GIST progression, MAX and DEPDC on chromosome 14q and 22q, respectively (14,34), were also investigated for alterations in CN and expression. CN decreases of MAX were observed in 18 of 30 cases regardless of the malignancy grade, but this did not affect its expression. CN decreases of DEPDC were observed in 15 cases, including nine malignant cases with KIT 557-558, in which some showed a reduction in expression. DMD on chromosome Xp has been suggested as a TSG involved in GIST development (13). SVs of DMD, which codes for dystrophin, were observed in 12 cases, all involving deletions, 10 of which were malignant cases with KIT 557-558 ( Supplementary Fig. S5). No clear relationship was observed between SVs and expression, but SVs may alter the protein structure of dystrophin, consequently influencing its function (13). Combining the results of mutations, SVs, and CDKNA, 11 of 12 cases, excluding case 12, showed driver alterations.
Expression data showed that three of the four cases with elevated telomerase reverse transcriptase (TERT) expression were malignant cases with KIT 557-558, but a comparison of expression levels showed no significant differences (P = 0.053) between these cases and the others. p14 ARF encoded by CDKNA directly interacts with MDM2 to antagonize its inhibition of p53 (35). Although TP53 mutations or SVs were not observed in the malignant cases with KIT 557-558 except in one case, the p53 inactivation signature was significantly higher (P = 0.019), along with the CIN signature (P = 0.030) and the hypoxia signature (P = 0.00096; Supplementary Data S4). These results indicate that genomic instability and hypoxia occurred in malignant GISTs with KIT 557-558.

Global Intergenic Hypomethylation Specific to Malignant GISTs with KIT 557-558
DNA hypermethylation at promoter regions and global hypomethylation have been observed in GISTs (17,18). We explored methylation changes in patients with GIST malignancies by performing methylation analysis of nine group A cases, nine other cases, and five cases of normal tissue. Principal component analysis of all probes revealed a different distribution in group A compared with the other groups ( Supplementary Fig. S6A). In contrast, probes for CpG islands near the TSS showed differences between tumor and normal tissues but no differences between tumors ( Supplementary Fig. S6B). Clustering results for all probes showed a hypomethylation cluster (C2) specific to group A (Fig. 4A). Next, to visualize the data on chromosomes, the probes were binned in 300kb lengths, the average β value was calculated, and the 300-kb bin data were classified by clustering (CB4 in Fig. 4B). When these results were plotted on the chromosome, hypomethylated regions were observed in group A across the entire chromosome ( Fig. 4C; Supplementary Fig. S7). The 300-kb bin β values in group A tended to be lower than those in other groups (Fig. 4D). Differences in expression data also distinguished group A from other groups: 926 and 1,497 probes showed increased and decreased expression, respectively, in group A (Fig. 5A). The volcano plot displayed significant expression differences in 74 probes, 66 of which corresponded to 62 genes by annotation ( Fig. 5B; Supplementary Data S5; Supplementary Table S3). Among them, SNAI had the highest significance and expression differences, and a comparison of SNAI expression between each group also showed significant differences (Fig. 5C).
In terms of the expression (Fig. 5A) and methylation (Fig. 4A) pattern as well as SNAI expression (Fig. 5C), case 12 is different from the other deletion  cases and may be in a high-risk group but with a low malignant status. Gene ontology enrichment analysis identified the involvement of genes related to response to DNA damage via p53 (Supplementary Fig. S8). The genes that showed these changes, hypomethylated regions, and genes corresponding to probes that showed differences in group A were plotted on the chromosomes, clearly revealing that hypomethylation was more common in the intergenic regions ( Fig. 4E;   Supplementary Fig. S9).

Discussion
Genomic instability is a hallmark of cancer (36) and is reflected by SVs, SNVs, and Indels (37), as well as DNA hypomethylation (38). Global DNA hypomethylation has been observed in malignant cases of GISTs (18), but there was no information about which exons were involved and what type of mutations were present. Among the malignant cases in this study, we found that GISTs carrying KIT 557-558 resulted in increased numbers of SVs, SNVs, and Indels, as well as global DNA hypomethylation, compared with cases carrying other exon 11 mutations ( Fig. 1; Fig. 4). The p53 and CIN expression signature scores were also significantly enhanced in the malignant GISTs with KIT 557-558. This phenomenon was not observed in low-grade, less malignant GISTs with KIT 557-558. We demonstrated that KIT 557-558 mutations are associated with increased genomic instability in malignant GISTs.
Among the KIT exon 11 mutations, those with deletions including KIT 557-558 showed increased ERK1/2 phosphorylation, increased cyclin D expression, and inactivation by RB protein phosphorylation compared with cases with point mutations, suggesting that the deletion mutations led to increased cell proliferation (39). Increased expression of ETV and CXCR has been observed in cases of liver metastasis, proposing a metastatic mechanism involving CXCL12/CXCR4-mediated cell proliferation and invasion (10). Furthermore, it was proposed that activation of oncogenic cell growth factors can result in DNA damage and replication stress, leading to genomic instability in cases of sporadic cancers (40). In other words, strong oncogene activation promotes tumorigenesis, which in turn causes genetic alterations in cell cycle-related genes, To the best of our knowledge, no other driver genes have been reported to alter biological properties as observed in the current results, which were attributed to different mutation types within the same exon. Broadly speaking, 746-750 deletion and L858R point mutation in EGFR, which are common pathologic mutations in lung cancer, are found in exons 19 and 21, respectively (41). These mutations are associated with differences in sensitivity to TKIs (41,42) and downstream signaling (43).
SVs alter gene expression (44,45). We classified SVs as driver SVs using SMAP, an evaluation protocol constructed by us based on their association with oncogenesis (21): tier 1 includes pathogenic SVs, tier 2 includes likely pathogenic SVs, and tier 3 includes SVs that are predicted to disrupt TSG function. In this study, four tier 2 SVs and 38 tier 3 SVs were detected, three of which (AKT, LRPB, and MGMT) were recurrent. AKT belongs to the AKT family of genes encoding serine/threonine protein kinases, and exhibits oncogenic transforming activity upon overexpression (46). Therefore, AKT was categorized as an oncogene in our driver gene classification. Conversely, AKT has been reported to inhibit cell proliferation and angiogenesis (47)(48)(49)(50). One of the two cases of AKT SVs (no. 2) involved the duplication of AKT intron 2 upstream to the 57-Mb region, whereas the other case (no. 6) featured an inversion over a 2.7-Mb region that included AKT intron 2 downstream to SMYD intron 1. AKT expression in these cases tended to be suppressed compared with that in the other cases ( Supplementary Fig. S10). LRPB is a member of the low-density lipoprotein receptor family and a putative TSG that is frequently inactivated to promote cell migration and invasion in various human cancers (51)(52)(53)(54)(55) Fig. S10).
In this study, we performed gene expression analysis using microarray with 60-nucleotide probes that mostly recognize the 3 ends of the transcripts. Therefore, if the probe recognizing regions are transcribed because of these deletions and translocations, gene expression could be observed. Further studies will reveal whether the potential driver SVs extracted in this study are actually involved in oncogenesis.
In addition to the strength of cell signaling, the genetic background may be involved in differences in genomic instability between malignant GISTs with KIT 557-558 and other exon 11 mutations. KIT exon 11 is the hotspot region in GISTs, with various types of mutations involving deletions, insertions, and substitutions. It would be interesting to investigate whether there is a genetic factor that causes and stabilizes KIT 557-558. This factor may also be involved in genomic instability by producing SVs, SNVs, Indels, and DNA hypomethylation. In the present study, the variants in the noncoding regions of the WGS data were not interpreted in detail, with the exception of the TERT driver mutations at the promoter region. Further detailed genomic and epige-nomic analyses of the noncoding regions will reveal the regulatory elements, promoters, and enhancers that alter gene expression in KIT 557-558.
Hsp90 inhibitors significantly prolong progression-free survival in advanced GISTs (19) and were recently approved for marketing in Japan. HSP90 inhibitors exert their antitumor effects by inhibiting the stabilization of KIT and PDGFRA proteins by Hsp90 (57). Hsp90 stabilizes HIF1α and induces hypoxia (58). Hsp90 inhibitors at the C-terminus degrade HIF1α and suppress hypoxia (59). In this study, the hypoxia score of the expression signature was higher in the malignant GISTs with KIT 557-558 (Fig. 3), indicating enhanced HIF1α activity. Therefore, it will be interesting to investigate the relationship between the efficacy of Hsp90 inhibitors and hypoxia scores.
Our comparative analysis revealed the significant upregulation of SNAI expression in malignant GISTs with KIT 557-558 (Fig. 5). SNAI encodes the transcription factor Snai2 (formerly known as Slug), one of three members of the snail zinc finger protein family (60). Snai2 promotes tumor cell metastasis through epithelial-mesenchymal transition (EMT), and Snai2 overexpression predicts poor prognosis in patients derived from various cancer types (60).
We and Ding and colleagues each previously reported the close correlation of SNAI expression with high-risk or metastatic GISTs, although the type of mutation was not considered (61,62). Cells undergoing EMT induces genomic instability via persistent proliferation (63). In addition, Snai2 is activated by HIF1α under hypoxia in the tumor microenvironment to promote metastasis (64,65). Hypoxia occurs in many solid tumors and contributes to genomic instability via aberrant DNA damage signaling and DNA repair, resulting in increased mutation rates (66). Taken together, tumor cells undergoing EMT with elevated SNAI expression and hypoxia scores may have caused genomic instability.
In this study, we performed a multi-omics analysis of WGS, genome-wide DNA methylation, and GEP data to elucidate global genomic and epigenomic abnormalities in patients with malignant GISTs carrying KIT 557-558. These results potentially provide evidence that patients with GIST with KIT 557-558 have a poor prognosis. However, this study had several limitations. First, this study had a small cohort size, and it was a single-institutional study. Second, the malignancy of GIST was evaluated using the modified NIH classification. Third, we did not completely eliminate the effect of imatinib pretreatment on the results, because of clonal selection. Finally, we did not perform functional analyses to evaluate the potential driver SVs. Therefore, further studies are needed in the future to validate these results.